Delete app.py

app.py

@@ -1,91 +0,0 @@
-#Importing all the necessary packages
-# import nltk
-# import librosa
-# import IPython.display
-# import torch
-import gradio as gr
-# from transformers import Wav2Vec2Tokenizer, Wav2Vec2ForCTC
-# nltk.download("punkt")
-#Loading the model and the tokenizer
-# model_name = "facebook/wav2vec2-base-960h"
-# tokenizer = Wav2Vec2Tokenizer.from_pretrained(model_name)#omdel_name
-# model = Wav2Vec2ForCTC.from_pretrained(model_name)
-
-# def load_data(input_file):
-#     """ Function for resampling to ensure that the speech input is sampled at 16KHz.
-#     """
-#     #read the file
-#     speech, sample_rate = librosa.load(input_file)
-#     #make it 1-D
-#     if len(speech.shape) > 1: 
-#         speech = speech[:,0] + speech[:,1]
-#     #Resampling at 16KHz since wav2vec2-base-960h is pretrained and fine-tuned on speech audio sampled at 16 KHz.
-#     if sample_rate !=16000:
-#         speech = librosa.resample(speech, sample_rate,16000)
-#     #speeches = librosa.effects.split(speech)
-#     return speech
-# def correct_casing(input_sentence):
-#     """ This function is for correcting the casing of the generated transcribed text
-#     """
-#     sentences = nltk.sent_tokenize(input_sentence)
-#     return (' '.join([s.replace(s[0],s[0].capitalize(),1) for s in sentences]))
-
-# def asr_transcript(input_file):
-#     """This function generates transcripts for the provided audio input
-#     """
-#     speech = load_data(input_file)
-#     #Tokenize
-#     input_values = tokenizer(speech, return_tensors="pt").input_values
-#     #Take logits
-#     logits = model(input_values).logits
-#     #Take argmax
-#     predicted_ids = torch.argmax(logits, dim=-1)
-#     #Get the words from predicted word ids
-#     transcription = tokenizer.decode(predicted_ids[0])
-#     #Output is all upper case
-#     transcription = correct_casing(transcription.lower())
-#     return transcription
-# def asr_transcript_long(input_file,tokenizer=tokenizer, model=model ):
-#     transcript = ""
-#     # Ensure that the sample rate is 16k
-#     sample_rate = librosa.get_samplerate(input_file)
-
-#     # Stream over 10 seconds chunks rather than load the full file
-#     stream = librosa.stream(
-#         input_file,
-#         block_length=20, #number of seconds to split the batch
-#         frame_length=sample_rate, #16000,
-#         hop_length=sample_rate, #16000
-#     )
-
-#     for speech in stream:
-#         if len(speech.shape) > 1:
-#             speech = speech[:, 0] + speech[:, 1]
-#         if sample_rate !=16000:
-#             speech = librosa.resample(speech, sample_rate,16000)
-#         input_values = tokenizer(speech, return_tensors="pt").input_values
-#         logits = model(input_values).logits
-
-#         predicted_ids = torch.argmax(logits, dim=-1)
-#         transcription = tokenizer.decode(predicted_ids[0])
-#         #transcript += transcription.lower()
-#         transcript += correct_casing(transcription.lower())
-#         #transcript += " "
-
-#     return transcript[:3800]
-import transformers 
-transformers.utils.move_cache()
-from transformers import pipeline
-p=pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h")
-
-def asr_transcript_long(input_file):
-    return p(input_file, chunk_length_s=10, stride_length_s=(2, 2))['text'].lower()
-
-gr.Interface(asr_transcript_long,
-             #inputs = gr.inputs.Audio(source="microphone", type="filepath", optional=True, label="Please record your voice"),
-             inputs = gr.inputs.Audio(source="upload", type="filepath", optional=True, label="Upload your audio file here"),
-             outputs = gr.outputs.Textbox(type="str",label="Output Text"),
-             title="English Automated Speech Summarization",
-             description = "This tool transcribes your audio to the text",
-             examples = [["sample 1.flac"], ["sample 2.flac"], ["sample 3.flac"],["TheDiverAnUncannyTale.mp3"]], 
-             theme="grass").launch()